It has recently been shown that
the information at the output of a dimension-reducing
transformation can be maximized
using probability density function projection (PDF projection) [1]}.
PDF projection estimates the distribution of the input data, simultaneously with a
dimension-reducing transformation that extracts the
latent variables [2]}, [3]}.
The method is more general than other methods of
non-linear dimension reduction (NLDR) [4]}, [5]}, [6]}, [7]}, [8]}.
Implementing PDF projection in a neural network architecture
is called projected belief network (PBN)
[9]}, [10]}, [11]}, [12]}.
As a result, the PBN is the most direct and general way to apply NLDR in a neural network architecture.
